Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer

By Juan Silva, A. Histace, O. Romain, X. Dray, B. Granado. International Journal of Computer Assisted Radiology and Surgery, 2014

1,213 citations recorded by the source corpus.

Subfields: Gastroenterology, Oncology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/d5e0460472a2ce2494f721e05f24898470e03e5e

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Citations in the corpus

CaraNet: context axial reverse attention network for segmentation of small medical objects (2021, methodology): “We choose five polyp datasets: ETIS [10], CVC-ClinicDB [11], CVC-ColonDB [12], EndoScene [13] and Kvasir [14].”
Cross-level Feature Aggregation Network for Polyp Segmentation (2023, methodology): “Table 3 provides the quantitative comparison between our model and six state-of-the-art methods on the ETIS [48] dataset.”
Distribution-Free, Risk-Controlling Prediction Sets (2021, methodology): “We evaluated our method with an experiment combining several open-source polyp segmentation datasets: Kvasir [45], Hyper-Kvasir [46], CVC-ColonDB and CVC-ClinicDB [47], and ETIS-Larib [48].”
Kvasir-SEG: A Segmented Polyp Dataset (2019, methodology): “e only few available polyp datasets that consist of ground truth and corresponding segmentation mask. These are CVC-ColonDB [24], ASU-Mayo Clinic Colonoscopy Video c Database [3], ETIS-Larib Polyp DB [23], and CVCClinic DB [2]. CVC-ColonDB [24] is the second largest database available and consists o”
DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation (2021, methodology): “Polyp Segmentation: For polyp segmentation task, we select ﬁve public polyp datasets including Kvasir [33], CVC-ColonDB (ColonDB) [34], EndoScene [35], ETIS [36], and CVC-ClinicDB (ClinicDB) [37].”
UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation (2021, background): “We validate our method with five famous polyp segmentation benchmarks, Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB and CVC-300 and achieve state-of-the-art performance among previous methods. ar X iv :2 10 7.”
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers (2021, methodology): “The images from top to bottom are from ClinicDB [10], ETIS [11], and ColonDB [12], which show that our model has better generalization ability.”
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images (2016, background): “The majority of available works on optical colonoscopy are focused on polyp detection (e.g. see [4,29,16,24,26,33]) and only few works address the problems of endoluminal scene segmentation.”
Shallow Attention Network for Polyp Segmentation (2021, methodology): “To evaluate the performance of the proposed SANet, five polyp segmentation datasets are adopted, including Kvasir [10], CVC-ClinicDB [2], CVC-ColonDB [3], EndoScene [18] and ETIS [16].”
A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation (2021, neutral): “2) We validate the extended architecture on a large range of datasets, i.e., Kvasir-SEG [4], CVC-ClinicDB [11], CVC-ColonDB [12], EITS-Larib [13], ASU-Mayo Clinic Colonoscopy Video Database [14] and CVC-VideoClinicDB [15], [16], and we compare our proposed approaches with the recent State-of-the-art”
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation (2020, methodology): “The same model was used to test against the ETIS-Larib dataset.”
Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information (2016, neutral): “[33]–[38], and narrow band imaging [39]–[41].”
Automatic Polyp Segmentation via Multi-scale Subtraction Network (2021, methodology): “We evaluate the proposed model on five benchmark datasets: CVC-ColonDB [22], ETIS [21], Kvasir [11], CVC-T [24] and CVC-ClinicDB [2].”
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge (2017, neutral): “[26]) and then use resulting information to define cues unique to polyps.”
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation (2021, methodology): “…the eﬀectiveness of TransFuse, four segmentation tasks with diﬀerent imaging modalities, disease types, target objects, target sizes, etc. are considered: 1) Polyp Segmentation , where ﬁve public polyp datasets are used: Kvasir [14], CVC-ClinicDB [2], CVC-ColonDB [24], EndoScene [27] and ETIS [21].”